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Interactive online learning for clinical entity recognition

Published: 26 June 2016 Publication History

Abstract

Named entity recognition and entity linking are core natural language processing components that are predominantly solved by supervised machine learning approaches. Such supervised machine learning approaches require manual annotation of training data that can be expensive to compile. The applicability of supervised, machine learning-based entity recognition and linking components in real-world applications can be hindered by the limited availability of training data. In this paper, we propose a novel approach that uses ontologies as a basis for entity recognition and linking, and captures context of neighboring tokens of the entities of interest with vectors based on syntactic and semantic features. Our approach takes user feedback so that the vector-based model can be continuously updated in an online setting. Here we demonstrate our approach in a healthcare context, using it to recognize body part and imaging modality entities within clinical documents, and map these entities to the right concepts in the RadLex and NCIT medical ontologies. Our current evaluation shows promising results on a small set of clinical documents with a precision and recall of 0.841 and 0.966. The evaluation also demonstrates that our approach is capable of continuous performance improvement with increasing size of examples. We believe that our human-in-the-loop, online learning approach to entity recognition and linking shows promise that it is suitable for real-world applications.

References

[1]
Lalithsena, S., et al. 2015. Feedback-Driven Radiology Exam Report Retrieval with Semantics. International Conference on Healthcare Informatics (ICHI), 233--242.
[2]
Mabotuwana, T., Lee, M. C. and Cohen-Solal, E. V. 2013. An ontology-based similarity measure for biomedical data application to radiology reports. Journal of Biomedical Informatics, 46(5), 857--868.
[3]
Settles, B. 2010. Active learning literature survey. University of Wisconsin, Madison, 52.55--66: 11.
[4]
Fu, Y., Zhu, X., and Li, B. 2013. A survey on instance selection for active learning. Knowledge and information systems, 35.2: 249--283.
[5]
Shen, W., Wang, J., and Han, J. 2015. Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering, 27.2: 443--460.
[6]
Savova, G. K., et al. 2010. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Informatics Association, 17.5, 507--513.
[7]
Hripcsak, G., et al. 1995. Unlocking clinical data from narrative reports: a study of natural language processing. Annals of internal medicine, 122.9, 681--688.
[8]
Aronson, A. R., and Lang, F. M. 2010. An overview of MetaMap: historical perspective and recent advances. Journal of the American Medical Informatics Association, 17.3, 229--236.
[9]
Patrick, J., and Li, M. 2010. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. Journal of the American Medical Informatics Association, 17.5, 524--527.
[10]
de Bruijn, B., et al. 2011. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. Journal of the American Medical Informatics Association, 18.5, 557--562.
[11]
Jiang, M., et al. 2011. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal of the American Medical Informatics Association, 18.5, 601--606.
[12]
Uzuner, Ö., et al. 2010. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Association, 18.5, 552--556.
[13]
Chen, Y., Lasko, T. A., Mei, Q., Denny, J. C. and Xu, H. 2015. A study of active learning methods for named entity recognition in clinical text. Journal of Biomedical Informatics, 58, 11--18.
[14]
Choi, J. D. and McCallum, A. 2013. Transition-based Dependency Parsing with Selectional Branching. Association for Computational Linguistics (ACL).
[15]
Tanenblatt, M. A., Coden, A. and Sominsky, I. L. 2010. The ConceptMapper Approach to Named Entity Recognition. In Language Resources and Evaluation Conference (LREC).
[16]
Von Ahn, L., et al. 2003. CAPTCHA: Using hard AI problems for security. Advances in Cryptology ---EUROCRYPT 2003, Springer Berlin Heidelberg, 294--311.

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  1. Interactive online learning for clinical entity recognition

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    cover image ACM Other conferences
    HILDA '16: Proceedings of the Workshop on Human-In-the-Loop Data Analytics
    June 2016
    93 pages
    ISBN:9781450342070
    DOI:10.1145/2939502
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Paxata: Paxata
    • tableau: Tableau Software
    • Trifacta: Trifacta
    • IBM: IBM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 June 2016

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    Author Tags

    1. electronic medical records
    2. entity linking
    3. entity recognition
    4. natural language processing
    5. online learning

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    SIGMOD/PODS'16
    Sponsor:
    • Paxata
    • tableau
    • Trifacta
    • IBM
    SIGMOD/PODS'16: International Conference on Management of Data
    June 26 - July 1, 2016
    California, San Francisco

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    HILDA '16 Paper Acceptance Rate 16 of 32 submissions, 50%;
    Overall Acceptance Rate 28 of 56 submissions, 50%

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